Class Project: Analysis of the S&P 500 to show historical trends based on past data¶

David Meduga S&DS 123

Introduction¶

The "The S&P500 is a market-capitalization-weighted index of 500 leading publicly traded companies in the U.S. The index actually has 503 components because three of them have two share classes listed."(investopedia) The S&P500 is a leading indicator in the finanical world that tells the world about how the economy is doing and the financial wellbeing of the top 500 companies. The object of this project is to make a analysis of the S&P500 and based on this data show investing strageties where it was most profitable to make a trade and either buy long or sell short. In my Final Project, I will be coding the adjusted returns from the S&P 500, taking the growth rate of specific stocks, use the top 9 stocks that makes up a majority of the S&P500 to show there growth over time, and using a basic investment strategy to show when it was the time to optimally purchase or sell a indidual stock in the S&P500.

The project focus is on the Financial world because the financial world encompases everything about the economy and a impact in the stock market is usually due to a world problem that causes a serious problem in the economy. Being able to track the stock market and see the changes in real time is staying ahead of the game and having this information is very valuable. This information leads to profits, real world impacts, and innovation. This is why I am doing a analysis on the S&P500 to see the historical trends and make trading decisions based on previous data.

There have been many other analysis of the the S&P500 ranging from corporations such as J.P. Morgan, Citidel, DRW Trading group. Each group has there own unique way of analysing the data and comes up with different conclusions on the most profitable trading strageties. They each use different techinques to bring about different amounts of profit and have a ranging varity of teams decitated to research the next stragety.

I obtained the data from the python package yfinance which is from yahoo finance. I used the link https://en.wikipedia.org/wiki/List_of_S%26P_500_companies to get the list of the S&P500 companies and there Ticker Symbols. I used the package yfinance

Data Wrangling¶

I am using adj_close = adjusted close of the stock,df= dataframe of the downloaded data from yfinance, fig = interavtive plots, top9stocks= top 9 stock in the S&P500, and rets = return rate. All other equations functions are explained as needed above the code.

Data installation¶

In [2]:
#run the pip install if you haven't already installed the data
#pip install yfinance
import pandas as pd
import yfinance as yf
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
import plotly.express as px
import plotly.graph_objects as go 
from datetime import datetime
%matplotlib inline

S&P500 Companies¶

In [3]:
#The Wikipedia URL of the S&P 500
sp_wiki_url = "https://en.wikipedia.org/wiki/List_of_S%26P_500_companies"

# Reading the HTML table into a list of DataFrames
sp_wiki_df_list = pd.read_html(sp_wiki_url)

# Only getting the information needed containing the company information
sp_df = sp_wiki_df_list[0]

# Extract the ticker symbol columns
sp_ticker_list = list(sp_df['Symbol'].values)
In [4]:
#now downloading the ticker into yahoo finance to extract the rest of the stocks data over a 24 year period 
df = yf.download(sp_ticker_list, start='2000-01-01', end='2024-01-01')

[*********************100%%**********************]  503 of 503 completed

5 Failed downloads:
['GEV', 'SOLV']: Exception("%ticker%: Data doesn't exist for startDate = 946702800, endDate = 1704085200")
['BRK.B']: Exception('%ticker%: No timezone found, symbol may be delisted')
['MSI']: JSONDecodeError('Expecting value: line 1 column 1 (char 0)')
['BF.B']: Exception('%ticker%: No price data found, symbol may be delisted (1d 2000-01-01 -> 2024-01-01)')
In [5]:
#Getting the Adjusted close for each Stock 
adj_close = df['Adj Close']
In [6]:
#putting it into a colum
adj_close.columns
Out[6]:
Index(['A', 'AAL', 'AAPL', 'ABBV', 'ABNB', 'ABT', 'ACGL', 'ACN', 'ADBE', 'ADI',
       ...
       'WTW', 'WY', 'WYNN', 'XEL', 'XOM', 'XYL', 'YUM', 'ZBH', 'ZBRA', 'ZTS'],
      dtype='object', name='Ticker', length=503)
In [7]:
adj_close.head()
Out[7]:
Ticker A AAL AAPL ABBV ABNB ABT ACGL ACN ADBE ADI ... WTW WY WYNN XEL XOM XYL YUM ZBH ZBRA ZTS
Date
2000-01-03 43.613018 NaN 0.846127 NaN NaN 8.992852 1.277778 NaN 16.274670 28.438288 ... NaN 11.505338 NaN 6.977998 18.328693 NaN 4.680299 NaN 25.027779 NaN
2000-01-04 40.281456 NaN 0.774790 NaN NaN 8.735913 1.270833 NaN 14.909397 26.999628 ... NaN 11.073110 NaN 7.138671 17.977619 NaN 4.586221 NaN 24.666668 NaN
2000-01-05 37.782803 NaN 0.786128 NaN NaN 8.719850 1.388889 NaN 15.204174 27.393782 ... NaN 11.659700 NaN 7.414122 18.957693 NaN 4.609741 NaN 25.138889 NaN
2000-01-06 36.344170 NaN 0.718097 NaN NaN 9.024966 1.375000 NaN 15.328291 26.644888 ... NaN 12.205120 NaN 7.345257 19.937765 NaN 4.570543 NaN 23.777779 NaN
2000-01-07 39.372864 NaN 0.752113 NaN NaN 9.121319 1.451389 NaN 16.072981 27.393782 ... NaN 11.803779 NaN 7.345257 19.879251 NaN 4.468629 NaN 23.513889 NaN

5 rows × 503 columns

In [8]:
#pulling the S&P500 from yahoo finance and then using an interactive plot to show 
sp_ticker_list = ['^GSPC']
df = yf.download(sp_ticker_list, start='2000-01-01', end='2024-01-01')

#An interactive plot using Plotly
fig = px.line(df, x=df.index, y='Adj Close', title='S&P500 Adjusted Close Price')
fig.update_layout(
    xaxis_title='Date',
    yaxis_title='Price',
    height=800,
    width=1000
)
fig.show()

[*********************100%%**********************]  1 of 1 completed
In [9]:
#this is the specific S&P500 data I am pulling to show over time the growth of the index 
df = yf.download("^GSPC", start = '1980-01-01')

[*********************100%%**********************]  1 of 1 completed
In [10]:
#this is a interactive Candlestick graph which shows the open, close, high, and low of the S&P500
fig = go.Figure(data = [go.Candlestick(
    x = df.index,
    open = df['Open'],
    close = df['Close'],
    high = df['High'],
    low =df['Low']
)
])
fig.update_layout(title='Candlestick of the S&P500')
fig.show()
In [11]:
#this is an interactive candlestick chart to measure when the stock was going up and down in a bigger version 
fig.update_layout(
    title='Interactive Candlestick Chart',
    width=1200,  # Set the width of the chart
    height=800,  # Set the height of the chart
    xaxis_rangeslider_visible=False,  # Hide the range slider
    showlegend=False # Hide the legend
)

Real World Impacts¶

In [12]:
#Historical Financial crashes
important_dates = {
    'The Oil Crisis: 1982-04-29',
    'The Tech Bubble: 2000-09-11',
    'Financial Crisis and Great Recession: 2007-10-12',
    'Covid 19 Pandemic:2020-03-20',
}
In [13]:
#using the interactive graph from earlier and putting a line when the financial Crashes happened
fig = go.Figure(data = [go.Candlestick(
    x = df.index,
    open = df['Open'],
    close = df['Close'],
    high = df['High'],
    low =df['Low'],
)])
fig.update_layout(
    title= "S&P500 Shocks", 
    yaxis_title= "S&P500 Stock",
    shapes = [
        dict(
        x0 = '1982-04-29', x1= '1982-04-29', y0=0, y1=1, xref = 'x', 
        yref= 'paper', line_width = 2), 
        dict(
        x0 = '2000-09-11', x1= '2000-09-11', y0=0, y1=1, xref = 'x', 
        yref= 'paper', line_width = 2),
        dict(
        x0 = '2007-10-12', x1= '2007-10-12', y0=0, y1=1, xref = 'x', 
        yref= 'paper', line_width = 2),
        dict(
        x0 = '2020-03-20', x1= '2020-03-20', y0=0, y1=1, xref = 'x', 
        yref= 'paper', line_width = 2)
        
    ], 
    annotations = [
        dict(x= '1982-04-29', y = 1.1 , xref ='x', yref = 'paper', showarrow = False, 
             xanchor = 'left', text = 'The Oil Crisis'),
        dict(x= '2000-09-11', y = 1.1, xref ='x', yref = 'paper', showarrow = False, 
             xanchor = 'left', text = 'The Tech Bubble'),
        dict(x= '2007-10-12', y = 1.1, xref ='x', yref = 'paper', showarrow = False, 
             xanchor = 'left', text = 'Financial Crisis and Great Recession'),
        dict(x= '2020-03-20', y = 1.1, xref ='x', yref = 'paper', showarrow = False, 
             xanchor = 'left', text = 'Covid 19 Pandemic')
    ]
)
fig.update_layout(xaxis_rangeslider_visible= False)

Now Let's view the top stocks that make up a large portion of the S&P500¶

In [14]:
top9stocks = dict(
    AAPL = "Apple stock", 
    AMZN = 'Amazon Stock',
    NVDA = 'NVIDIA',
    GOOGL = 'Alphabet Class A',
    TSLA = 'Tesla',
    GOOG =  'Alphabet Class C',
    META = 'Meta Platforms Class A',
    MSFT = 'Miscroft Stock ',
    UNH = 'United Health Group'
)
In [15]:
list(top9stocks.keys())
Out[15]:
['AAPL', 'AMZN', 'NVDA', 'GOOGL', 'TSLA', 'GOOG', 'META', 'MSFT', 'UNH']
In [16]:
df_top9 = yf.download(list(top9stocks.keys()))

[*********************100%%**********************]  9 of 9 completed
In [17]:
adj_close_top9 = df_top9['Adj Close']
adj_close_top9 = adj_close_top9.dropna()

Top 9 Stocks Adjusted Close Over Time¶

In [18]:
adj_close_top9.dropna().plot(subplots = True, figsize = (12,10));
No description has been provided for this image
In [19]:
#A nice version of all the stock and there respected name
for key, value in top9stocks.items():
    print(f"{key:8s} | {value}")

AAPL     | Apple stock
AMZN     | Amazon Stock
NVDA     | NVIDIA
GOOGL    | Alphabet Class A
TSLA     | Tesla
GOOG     | Alphabet Class C
META     | Meta Platforms Class A
MSFT     | Miscroft Stock 
UNH      | United Health Group
In [20]:
#the dtype of the stocks 
adj_close_top9.info()

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 2999 entries, 2012-05-18 to 2024-04-19
Data columns (total 9 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   AAPL    2999 non-null   float64
 1   AMZN    2999 non-null   float64
 2   GOOG    2999 non-null   float64
 3   GOOGL   2999 non-null   float64
 4   META    2999 non-null   float64
 5   MSFT    2999 non-null   float64
 6   NVDA    2999 non-null   float64
 7   TSLA    2999 non-null   float64
 8   UNH     2999 non-null   float64
dtypes: float64(9)
memory usage: 234.3 KB
In [21]:
#roundung to the scond digit
adj_close_top9.describe().round(2)
Out[21]:
Ticker AAPL AMZN GOOG GOOGL META MSFT NVDA TSLA UNH
count 2999.00 2999.00 2999.00 2999.00 2999.00 2999.00 2999.00 2999.00 2999.00
mean 70.38 76.99 64.34 64.41 165.14 135.02 103.73 84.41 236.54
std 58.51 55.04 40.36 39.77 101.73 111.24 155.68 105.37 159.58
min 11.98 10.41 13.92 13.99 17.71 21.51 2.61 1.74 42.57
25% 24.13 21.34 29.21 29.70 81.98 40.37 5.26 14.35 100.27
50% 41.15 77.25 53.48 53.77 159.09 90.07 42.74 20.17 211.89
75% 129.02 122.62 95.52 95.16 212.25 233.31 137.71 180.94 379.28
max 197.86 189.05 160.79 159.41 527.34 429.37 950.02 409.97 548.93
In [22]:
adj_close_top9.mean()
Out[22]:
Ticker
AAPL      70.383720
AMZN      76.986150
GOOG      64.340510
GOOGL     64.413177
META     165.136826
MSFT     135.015776
NVDA     103.728866
TSLA      84.414281
UNH      236.542746
dtype: float64
In [23]:
#putting all the data gathered together
adj_close_top9.aggregate(['min', 'mean', 'std', 'median', 'max']).round(2)
Out[23]:
Ticker AAPL AMZN GOOG GOOGL META MSFT NVDA TSLA UNH
min 11.98 10.41 13.92 13.99 17.71 21.51 2.61 1.74 42.57
mean 70.38 76.99 64.34 64.41 165.14 135.02 103.73 84.41 236.54
std 58.51 55.04 40.36 39.77 101.73 111.24 155.68 105.37 159.58
median 41.15 77.25 53.48 53.77 159.09 90.07 42.74 20.17 211.89
max 197.86 189.05 160.79 159.41 527.34 429.37 950.02 409.97 548.93
In [24]:
adj_close_top9.diff().head()
Out[24]:
Ticker AAPL AMZN GOOG GOOGL META MSFT NVDA TSLA UNH
Date
2012-05-18 NaN NaN NaN NaN NaN NaN NaN NaN NaN
2012-05-21 0.934280 0.2130 0.341470 0.343093 -4.195549 0.385847 0.048159 0.080667 1.297382
2012-05-22 -0.130314 -0.1390 -0.331507 -0.333083 -3.026787 0.008038 -0.034400 0.135333 0.141380
2012-05-23 0.410906 0.0975 0.215691 0.216717 0.998940 -0.522503 0.068798 0.014667 -0.299397
2012-05-24 -0.158438 -0.1020 -0.144458 -0.145144 1.028908 -0.032152 -0.075677 -0.049333 0.715218
In [25]:
adj_close_top9.pct_change().round(3).head()
Out[25]:
Ticker AAPL AMZN GOOG GOOGL META MSFT NVDA TSLA UNH
Date
2012-05-18 NaN NaN NaN NaN NaN NaN NaN NaN NaN
2012-05-21 0.058 0.020 0.023 0.023 -0.110 0.016 0.017 0.044 0.029
2012-05-22 -0.008 -0.013 -0.022 -0.022 -0.089 0.000 -0.012 0.071 0.003
2012-05-23 0.024 0.009 0.014 0.014 0.032 -0.022 0.025 0.007 -0.006
2012-05-24 -0.009 -0.009 -0.010 -0.010 0.032 -0.001 -0.027 -0.024 0.016

Bar Graph of the Percentage Change¶

In [26]:
adj_close_top9.pct_change().mean().plot(kind = "bar", figsize = (5,5));
No description has been provided for this image
In [27]:
#shifting the data by 1 row down
adj_close_top9.shift(1)
Out[27]:
Ticker AAPL AMZN GOOG GOOGL META MSFT NVDA TSLA UNH
Date
2012-05-18 NaN NaN NaN NaN NaN NaN NaN NaN NaN
2012-05-21 16.036409 10.692500 14.953949 15.025025 38.189480 23.528458 2.770252 1.837333 44.901154
2012-05-22 16.970690 10.905500 15.295419 15.368118 33.993931 23.914305 2.818411 1.918000 46.198536
2012-05-23 16.840376 10.766500 14.963912 15.035035 30.967144 23.922342 2.784012 2.053333 46.339916
2012-05-24 17.251282 10.864000 15.179603 15.251752 31.966084 23.399839 2.852809 2.068000 46.040520
... ... ... ... ... ... ... ... ... ...
2024-04-15 176.550003 186.130005 159.190002 157.729996 511.899994 421.899994 881.859985 171.050003 439.200012
2024-04-16 172.690002 183.619995 156.330002 154.860001 500.230011 413.640015 860.010010 161.479996 445.630005
2024-04-17 169.380005 183.320007 156.000000 154.399994 499.760010 414.579987 874.150024 157.110001 468.890015
2024-04-18 168.000000 181.279999 156.880005 155.470001 494.170013 411.839996 840.349976 155.449997 478.989990
2024-04-19 167.039993 179.220001 157.460007 156.009995 501.799988 404.269989 846.710022 149.929993 493.179993

2999 rows × 9 columns

In [28]:
#Gettign the return rate of each stock
rets = np.log(adj_close_top9  / adj_close_top9.shift (1))

Return Rate Graph for Top 9 Stock in the S&P500¶

In [29]:
rets.cumsum().apply(np.exp).plot(figsize = (10,5));
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In [30]:
#each graph indidually
rets.cumsum().apply(np.exp).plot(subplots = True, figsize = (12,10));
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Resampling the data¶

In [31]:
# Resampling the data to every week the data will show
adj_close_top9.resample('1w', label = 'right').last().head()
Out[31]:
Ticker AAPL AMZN GOOG GOOGL META MSFT NVDA TSLA UNH
Date
2012-05-20 16.036409 10.6925 14.953949 15.025025 38.189480 23.528458 2.770252 1.837333 44.901154
2012-05-27 17.001230 10.6445 14.733027 14.803053 31.876179 23.359653 2.843637 1.987333 46.672581
2012-06-03 16.961918 10.4110 14.221195 14.288789 27.690619 22.869303 2.747319 1.876667 45.774391
2012-06-10 17.546375 10.9240 14.457061 14.525776 27.071278 23.833916 2.779425 2.005333 48.236088
2012-06-17 17.359219 10.9175 14.060049 14.126877 29.978193 24.131346 2.818411 1.994000 49.165642
In [32]:
# Resampling the data to every month the data will show
adj_close_top9.resample('1m', label = 'right').first().head()
Out[32]:
Ticker AAPL AMZN GOOG GOOGL META MSFT NVDA TSLA UNH
Date
2012-05-31 16.036409 10.6925 14.953949 15.025025 38.189480 23.528458 2.770252 1.837333 44.901154
2012-06-30 16.961918 10.4110 14.221195 14.288789 27.690619 22.869303 2.747319 1.876667 45.774391
2012-07-31 17.915249 11.4660 14.457560 14.526276 30.737387 24.565413 3.084428 2.026667 46.961941
2012-08-31 18.347324 11.6045 15.757935 15.832833 20.857868 23.640997 3.070669 1.750000 42.746555
2012-09-30 20.495808 12.3940 16.962421 17.043043 17.711208 24.590599 3.045443 1.876000 45.542900
In [33]:
# Newly resampled graph to show every month return rate
rets.cumsum().apply(np.exp).resample("1m", label = "right").last().plot(figsize = (10,5));
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Total Price of Top 9 Stock in the S&P500¶

In [34]:
top9_stocks = dict(
    AAPL = "Apple stock", 
    AMZN = 'Amazon Stock',
    NVDA = 'NVIDIA',
    GOOGL = 'Alphabet Class A',
    TSLA = 'Tesla',
    GOOG =  'Alphabet Class C',
    META = 'Meta Platforms Class A',
    MSFT = 'Miscroft Stock ',
    UNH = 'United Health Group'
)
In [35]:
#dowloading the respective data
df1 = yf.download(list(top9_stocks.keys()))

[*********************100%%**********************]  9 of 9 completed
In [36]:
#dropping the NA's
df1= df1['Open'].dropna()
In [37]:
#The most recent data
df1.tail()
Out[37]:
Ticker AAPL AMZN GOOG GOOGL META MSFT NVDA TSLA UNH
Date
2024-04-12 174.259995 187.720001 159.404999 157.960007 517.750000 424.049988 896.989990 172.339996 440.339996
2024-04-15 175.360001 187.429993 160.279999 158.860001 516.719971 426.600006 890.979980 170.240005 442.000000
2024-04-16 171.750000 183.270004 155.639999 154.190002 498.109985 414.570007 864.330017 156.740005 476.769989
2024-04-17 169.610001 184.309998 157.190002 155.619995 503.100006 417.250000 883.400024 157.639999 478.600006
2024-04-18 168.029999 181.470001 156.925003 155.339996 499.820007 410.630005 849.700012 151.250000 486.130005
In [38]:
#now let look at Amazon Stock
symbol = 'AMZN'
window = 20 

df1['min'] = df1[symbol].rolling(window=window).min()
df1['mean'] = df1[symbol].rolling(window=window).mean()
df1['std'] = df1[symbol].rolling(window=window).std()
df1['median'] = df1[symbol].rolling(window=window).median()
df1['max'] = df1[symbol].rolling(window=window).max()
df1['ewma'] = df1[symbol].ewm(halflife= .5, min_periods = window).mean()

Amazon stock price change over a year¶

In [39]:
ax = df1[['min', 'mean', 'max']].iloc[-200:].plot(
    figsize = (10,6), style = ['g--', 'r--', 'g--'], 
    lw=.6
)
df1[symbol].iloc[-200:].plot(ax=ax, lw= 2.0);
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Techinical Analysis Using Simple Moving Average¶

In [40]:
#Using rolling Statistics
df1['SMA1'] = df1[symbol].rolling(window=42).mean()
df1['SMA2'] = df1[symbol].rolling(window=252).mean()
In [41]:
df1[[symbol, 'SMA1', 'SMA2']].tail()
Out[41]:
Ticker AMZN SMA1 SMA2
Date
2024-04-12 187.720001 176.936191 141.374326
2024-04-15 187.429993 177.405238 141.725437
2024-04-16 183.270004 177.740000 142.047659
2024-04-17 184.309998 178.066905 142.369683
2024-04-18 181.470001 178.370000 142.677302
In [42]:
df1.dropna(inplace = True)
In [43]:
#this is 1 is holding long and 2 is short
df1['positions'] = np.where(df1['SMA1'] > df1['SMA2'], 1, -1)

Ploting Simple Moving Average¶

In [44]:
ax= df1[[symbol, 'SMA1', 'SMA2', 'positions']].plot(figsize= (10,6),
        secondary_y = 'positions')
ax.get_legend().set_bbox_to_anchor((0.25, 0.85))
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Individual Stock Comparison¶

In [45]:
# Two Stocks competing in the same industry
stock1 = 'AAPL'
stock2 = 'MSFT'
In [46]:
df3 = yf.download([stock1, stock2])

[*********************100%%**********************]  2 of 2 completed
In [47]:
df3.dropna(inplace=True)
In [48]:
#comparison the two stock individualy
df3['Adj Close'].plot(subplots=True, figsize = (10,6));
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In [49]:
df3 = df3['Adj Close']

Graph of Apple and Miscroft Stock rise Over Time¶

In [50]:
# Comparision of the stocks on the same graph
df3.loc['2010':'2019'].plot(secondary_y=stock2, figsize= (12,6));
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In [51]:
df_small = df3.loc['2010':'2019']
In [52]:
#gathering the Return Rate of the Stocks 
rets1 = np.log(df_small / df_small.shift(1))
In [53]:
rets1.dropna(inplace=True)
In [54]:
#Plotting the return rate
rets1.plot(subplots = True, figsize = (10,6));
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In [55]:
# Average increase
reg = np.polyfit(rets1[stock1], rets1[stock2], deg =1)
ax = rets1.plot(kind= 'scatter', x = stock1, y=stock2, figsize = (10,6))
ax.plot(rets1[stock1], np.polyval(reg, rets1[stock1]), 'r', lw=2);
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Correlation¶

In [56]:
rets1.corr()
Out[56]:
Ticker AAPL MSFT
Ticker
AAPL 1.000000 0.458318
MSFT 0.458318 1.000000
In [57]:
#where the data from above shows on a graph with flucations
ax = rets1[stock1].rolling(window=252).corr(rets1[stock2]).plot(figsize=(10,6))
ax.axhline(rets1.corr().iloc[0,1], c = 'r');
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In [67]:
import plotly.io as pio
pio.renderers.keys()
import plotly.io as pio
pio.renderers.default = 'jupyterlab'
pio.renderers.default = 'notebook'

Conclusion¶

In conclusion, the finding of doing the analysis of the S&P500 include: How different stocks are corelated with one another, how less frequent simple trading strategies actually trades, and how indidual stock moves in a general direction. From this analysis, I know how to use the package yfinance in python and do a deep dive into a stock analysis getting the returns and growth rate over time. This is very helpful in insight into investing in any stock. This code is very verstile is discovering a trend among stocks and can compare stocks to each other to find their corelation rate to diversify their portfolio. I also learned that every substantial dip/loss when analyzing the S&P500 is usually due a substantial world diaster or real world economy problem. Knowing this information being able to stay informed throughout news channels and articles will help me better understand the stock market and know how a certian event will influence the market. This analysis will help others to look at stocks they are interested in and be able to see inforamtion in a orgainzed way to help better the individual investments. Using Python we are able to explore all this data and discover model to optimize our investments.

Reflection¶

This was a fun assignment to do because I was able to see trends I never thought existed and be able to see how stocks change over time. This was a fun assignment to put together of how useful python is in the real world and how different profession do this for a living. For someone looking to invest this was a fun assignment on using data to find the best time to buy/sell a investment. It was difficult to plot the data and use the package yfinance effectivly but it was a good expereince in dealing with data.

Changes for Final Version¶

The changes I want to make for the final version include

  1. Adding a search feature where you enter in a stock and it will return you all the data through that one prompt
  2. A simulator where you can see how much money you will have if you invested a certain amount at a certain time in a stock.